
"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      ['''image_processor''', '''tokenizer''']
       UpperCamelCase_		:       Any				=      '''ViTImageProcessor'''
       UpperCamelCase_		:       Optional[int]				=      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				List[str]=None							,   **UpperCAmelCase_			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                         " instead."							,   UpperCAmelCase_							,   )
                     UpperCamelCase       			=		kwargs.pop("feature_extractor"      )

              UpperCamelCase       			=		image_processor if image_processor is not None else feature_extractor
              if image_processor is None:
                     raise ValueError("You need to specify an `image_processor`."      )
              if tokenizer is None:
                     raise ValueError("You need to specify a `tokenizer`."      )

              super().__init__(UpperCAmelCase_							,   UpperCAmelCase_      )






       def __call__(	self			:				List[Any]							,   UpperCAmelCase_			:				Optional[Any]=None							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Dict=None							,   UpperCAmelCase_			:				Union[str, Any]=None							,   **UpperCAmelCase_			:				List[str]      )->	Optional[Any]:







              """simple docstring"""
              if text is None and visual_prompt is None and images is None:
                     raise ValueError("You have to specify either text, visual prompt or images."      )

              if text is not None and visual_prompt is not None:
                     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."      )

              if text is not None:
                     UpperCamelCase       			=		self.tokenizer(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_      )

              if visual_prompt is not None:
                     UpperCamelCase       			=		self.image_processor(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_      )

              if images is not None:
                     UpperCamelCase       			=		self.image_processor(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_      )

              if visual_prompt is not None and images is not None:
                     UpperCamelCase       			=		{
                         "pixel_values": image_features.pixel_values,
                         "conditional_pixel_values": prompt_features.pixel_values,
                     }
                     return encoding
              elif text is not None and images is not None:
                     UpperCamelCase       			=		image_features.pixel_values
                     return encoding
              elif text is not None:
                     return encoding
              elif visual_prompt is not None:
                     UpperCamelCase       			=		{
                         "conditional_pixel_values": prompt_features.pixel_values,
                     }
                     return encoding
              else:
                     return BatchEncoding(data=dict(**UpperCAmelCase_      )							,   tensor_type=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   *UpperCAmelCase_			:				Tuple							,   **UpperCAmelCase_			:				Optional[Any]      )->	int:







              """simple docstring"""
              return self.tokenizer.batch_decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   *UpperCAmelCase_			:				Tuple							,   **UpperCAmelCase_			:				Any      )->	Dict:







              """simple docstring"""
              return self.tokenizer.decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[Any]:







              """simple docstring"""
              warnings.warn(
                  "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,   UpperCAmelCase_							,   )
              return self.image_processor_class






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	int:







              """simple docstring"""
              warnings.warn(
                  "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,   UpperCAmelCase_							,   )
              return self.image_processor







"""simple docstring"""


def 						lowerCamelCase__      (				UpperCAmelCase_ = 60_08_51_47_51_43					)->				int:







       """simple docstring"""

       try:
              UpperCamelCase       			=		int(UpperCAmelCase_					)
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."					)
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."					)
       UpperCamelCase       			=		2
       UpperCamelCase       			=		0
       if n == 2:
              return 2
       while n > 2:
              while n % i != 0:
                     i += 1
              UpperCamelCase       			=		i
              while n % i == 0:
                     UpperCamelCase       			=		n // i
              i += 1
       return int(UpperCAmelCase_					)


if __name__ == "__main__":
      print(F'''{solution() = }''')





